Context: The sandy-savanna ecosystem "Mussununga", a natural ecosystem that occurs as patches throughout the Atlantic Forest domain, is threatened by anthropogenic factors and biological invasions of Australian Acacia species. Habitat degradation in the Atlantic Forest domain and extensive road networks could facilitate Acacia invasion into Mussununga. Objectives: We investigated whether: (a) landscape permeability (measured by effective conductance) facilitates Acacia invasion; (b) forest fragments are barriers, and roads and highways are corridors for invasive spread of Acacia; and (c) size and shape of Mussununga patches play a role in biological invasion. Methods: Acacia invasion was investigated in 32 Mussununga sites within the Atlantic Forest domain. We tested the effect of a set of landscape permeability scenarios based on circuit analysis and nine other metrics of landscape structure on Acacia occurrence using three buffer-zone sizes (0.5, 1, and 2 km). Results: The likelihood of Acacia invasion significantly increased with landscape permeability. The best-fitting landscape permeability scenario designated road networks as corridors, intact forests and water surfaces as barriers, and degraded habitats as non-barriers. We also found that Mussununga areas within a 0.5 km buffer negatively affected the biological invasion by Acacia. Conclusions: Extensive habitat degradation by deforestation and dense road networks facilitate Acacia invasion into sandy-savanna Mussununga ecosystems. Landscape permeability may be used as a risk-assessment tool for biological invasion by Acacia species. Mussununga patches can be protected from Acacia invasion | 599 Applied Vegetation Science HERINGER Et al.

The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyper-spectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3-5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.

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